What is the core technology of BMS power lithium battery management system?

What is the core technology of bMS?

Recently, I saw a billboard of a domestic company claiming to fully grasp the software and hardware technology of the power lithium battery management system (bMS) because of the use of the underlying software such as the AUTOSAR software architecture, to reach the world's advanced level, and to adopt multiple balance control capabilities. Very eye-catching. Are these things the core technology of bMS?

Usually the bMS system usually includes a test module and an operation control module.

Testing refers to measuring the voltage, current and temperature of the battery cell and the voltage of the battery pack, and then sending these signals to the computing module to solve and issue instructions. So the operation control module is the brain of bMS. The control module generally includes hardware, basic software, runtime environment (RTE) and application software. The core part--the use of software. The environment developed with Simulink is generally divided into two parts: battery state estimation algorithm and fault diagnosis and protection. State estimation includes SOC (StateOfCharge), SOP (StateOfPower), SOH (StateofHealth), and balance and thermal management.

Battery state estimation is usually to estimate SOC, SOP and SOH. SOC (state of charge) simply means how much power is left in the battery; SOC is the most critical parameter in bMS, because everything else is based on SOC, so its accuracy and robustness (also called error correction ability) is extremely important. If there is no accurate SOC, adding more protection functions will not make the bMS work normally, because the battery will often be in a protected state, and the life of the battery cannot be extended.

In addition, the estimation accuracy of SOC is also very critical. The higher the accuracy, the higher the cruising range for the battery with the same capacity. Therefore, high-precision SOC estimation can effectively reduce the required battery cost. For example, Chrysler's Fiat 500ebEV can discharge SOC=5% all the time. It became the electric car with the longest cruising range at that time.

The figure below is an example of the robustness of the algorithm. The battery is a lithium iron phosphate battery. Its SOCvsOCV curve only changes about 2-3mV in the SOC from 70% to 95%. The measurement error of the voltage sensor is 3-4mV. In this case, we deliberately let the initial SOC have a 20% error, and see if the algorithm can correct the 20% error. If there is no error correction function, SOC will follow the curve of SOCI. The SOC output by the algorithm is CombinedSOC, which is the blue solid line in the figure. CalculatedSOC is the real SOC deduced back based on the final verification result.

SOP is the maximum discharge and charge power that the battery can supply at the next moment, such as the next 2 seconds, 10 seconds, 30 seconds and continuous high current. Of course, the influence of the continuous large current on the fuse should also be taken into account.

Accurate estimation of SOP can maximize battery utilization efficiency. For example, when braking, it can absorb as much feedback energy as possible without damaging the battery. When accelerating, more power can be supplied to obtain greater acceleration without damaging the battery. At the same time, it can also ensure that the car will not lose power due to undervoltage or overcurrent protection during driving even when the SOC is very low. In this way, the so-called first-level protection and second-level protection are all passing in front of the precise SOP. Not that protection is unimportant. Protection is always needed. But it cannot be the core technology of bMS. Accurate SOP estimation is especially critical for low temperatures, old batteries, and very low SOC. For example, for a group of well-balanced battery packs, when the SOC is relatively high, the SOC difference between them may be small, such as 1-2%. But when the SOC is very low, the voltage of a cell will drop rapidly. The voltage of this cell is even more than 1V lower than other battery voltages. To ensure that the voltage of each battery cell is always not lower than the minimum voltage given by the battery supplier, the SOP must accurately estimate the maximum output power of the battery cell whose voltage drops rapidly at the next moment to limit the use of the battery and protect the battery. The core of estimating SOP is to estimate each equivalent impedance of the battery online in real time.

SOH refers to the state of health of the battery. It includes two parts: ampere-hour capacity and power changes. It is generally believed that when the ampere-hour capacity decreases by 20% or the output power decreases by 25%, the battery life is over. However, this does not mean that the car cannot be driven. For pure electric vehicles EV, the estimation of the ampere-hour capacity is more important because it has a direct relationship with the cruising range and the power limit is only important when the SOC is low. For HEV or PHEV, the change of power is more critical because the battery's ampere-hour capacity is relatively small, and the power that can be supplied is limited, especially at low temperature. The requirements for SOH are both high precision and robustness. And SOH without robustness is meaningless. Accuracy below 20% is meaningless. The estimation of SOH is also based on the estimation of SOC. So the SOC algorithm is the core of the algorithm. The battery state estimation algorithm is the core of bMS. Everything else is at the service of this algorithm. So when someone claims to have broken through or mastered the core technology of bMS, you should ask him what he has done in bMS? Is it algorithm or active equalization or only bMS hardware and underlying software? Or just propose a way to structure bMS?

Some people say that Tesla is awesome because its bMS can manage 7104 batteries. Is this where it's awesome? Is it really managing 7104 cells? Tesla model S actually uses 7104 batteries, but there are only 96 batteries in series, and only one battery in parallel, no matter how many batteries you connect in parallel. why? Because the battery packs of other companies only count the number of series connection instead of the number of parallel connection. Why should Tesla be special? In fact, if you understand Tesla's algorithm, you will understand that Tesla's algorithm not only requires a large amount of working condition data to be calibrated, but also cannot guarantee the estimation accuracy under any circumstances, especially after the battery ages. Of course, Tesla's algorithm is much better than almost all domestic bMS algorithms. The domestic bMS algorithm is almost always the method of current integration plus open circuit voltage, using the open circuit voltage to calculate the initial SOC, and then using the current integration to calculate the change of SOC. The problem is that if the voltage at the starting point is wrong, or the ampere-hour capacity is inaccurate, wouldn't it be corrected until the voltage overflows again? Will the voltage at the starting point be wrong? Relevant experience tells us that it will, although the probability is very low. If you want to be foolproof, you can't just rely on the accurate voltage at the starting point to ensure the correctness of the starting SOC.

What kind of algorithm is the core technology?

From a control point of view, a good algorithm should have two criteria: accuracy and robustness (error correction capability). The higher the accuracy, the better the reason is not much to say here. The above-mentioned current integration plus open circuit voltage is actually error correction with open circuit voltage, but this method is obviously far less robust than online real-time error correction. This is the reason why large foreign companies are using online real-time estimation of open circuit voltage to realize real-time error correction on-line.

Why the emphasis on real-time online estimation here? What are its benefits? All equivalent parameters of the battery are estimated through real-time online estimation, thereby accurately estimating the state of the battery pack. Real-time online estimation greatly simplifies the calibration work of the battery. This makes precise control of the state of the less consistent battery pack a reality. Real-time online estimation makes it possible to maintain high precision (Accuracy) and super error correction capability (Robustness or error correction capability) whether it is a new battery or an aging battery.

Some people in China often don't understand what other people's algorithms are. It is inappropriate to say that a certain manufacturer has mastered the core technology of bMS when they process some parts of bMS for a certain factory. Those bulky publications that cost tens of thousands of dollars to buy comment on the advantages and disadvantages of bMS from various manufacturers, but do not care about the differences in the algorithm of each bMS or the difference in core technology, the actual meaning is too small. Just looking at whether to supply bMS to a well-known OEM is considered awesome, and I don't know what to supply in bMS. I don't know if there is a kind of psychology of admiration for foreigners.

What are the characteristics of the best bMS in the world at present? It can estimate the battery parameters of the battery pack online in real time to accurately estimate the SOC, SOP, and SOH of the battery pack, and can correct the error of the initial SOC exceeding 10% and the error or percentage of the ampere-hour capacity exceeding 20% in a short time How many current measurement errors. General Motors of the United States did an experiment to detect the robustness of the algorithm when it developed Volt 6 years ago: remove one string of battery packs connected in parallel with 3 series, and then increase the internal resistance by 1/3, Ah capacity is reduced by 1/3. But bMS doesn't know. The result is that SOC, SOP are all corrected in less than 1 minute and SOH is then accurately estimated. This not only shows the strong error correction ability of the algorithm, but also shows that the algorithm can keep the estimation accuracy unchanged throughout the battery life cycle.

As far as the computer is concerned, if a blue screen appears, we usually just need to restart the computer. However, for a car, even a one in ten thousand chance of breaking down is intolerable. Therefore, unlike publishing articles, automotive electronics must be guaranteed to work under any circumstances. To make a good algorithm requires a lot of energy to deal with those situations where the probability of occurrence is only one in a thousand or one in ten thousand. This is the only way to ensure that nothing goes wrong. For example, when a car is driving on a winding mountain road at high speed, the battery model that everyone knows will fail. This is because the continuous high current will quickly consume the charged ions on the electrode surface, and the internal ions will not have time to diffuse out, and the battery voltage will drop sharply. The estimated SOC will have a large error or even an error of more than 10%. The precise mathematical model is the diffusion equation mentioned in textbooks of mathematical physics methods. But it cannot be used in the car because the computational complexity of the numerical solution is too large. The CPU computing power of bMS is not enough. This is not only an engineering problem, but also a math and physics problem. Dealing with such technical difficulties can resolve almost all known polarization problems that affect battery state estimation.

The state estimation technology of bMS is the core technology of bMS. Although 6 years have passed, there is still no supplier in the world that can achieve such a high level of precision and robustness to ensure that the battery works foolproof. Even Tesla, which is currently red and purple, is far behind. This is not bragging. Tesla fans must have heard of Tesla being dragged away on the streets of Beijing. Tesla's algorithm also cannot guarantee accuracy and robustness as batteries age. Only an algorithm that can guarantee high precision and high robustness is the killer! Without such technology, how to overtake on a curve?